Using a probabilistic model for detecting an object in visual data

ABSTRACT

A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

BACKGROUND

Object recognition can be performed to detect presence of a particularobject in an image. Object detection can be based on matching featuresin the image with features of a representation of the particular object,where the representation can be a model of the particular object. Amatching procedure may indicate that the particular object is in theimage if a number of matching features between the image and therepresentation of the object exceeds a fixed threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are described with respect to the following figures:

FIG. 1 is a block diagram of an example arrangement that includes anobject detection system according to some implementations;

FIG. 2 is a flow diagram of an object recognition procedure inaccordance with some implementations;

FIG. 3 is a flow diagram of an object recognition procedure inaccordance with further implementations;

FIG. 4 is a flow diagram of a process of building a probabilistic modelfor a given object, in accordance with some implementations; and

FIG. 5 is a flow diagram of a geometric consistent procedure useable inthe object recognition procedure of FIG. 3, in accordance with someimplementations.

DETAILED DESCRIPTION

To perform object recognition to detect presence of an object in visualdata (e.g. an image, video data, etc.), an object recognition procedurecan extract features from the visual data for comparison with respectivefeatures of a given representation (e.g. model) of the object. Examplesof objects that are to be detected in visual data can include thefollowing: photographs, posters, product packaging (e.g. boxes, bottles,cans, etc.), billboards, buildings, monuments, vehicles, landscapes, andso forth.

A match is considered to have occurred (in other words, the object isconsidered to be present in the visual data) if the number of matchingfeatures between the visual data and the representation of the objectexceeds some specified threshold. Different types of objects can includedifferent numbers of features that are useable for matching torespective representations of objects. For example, a first type ofobject can include a first number of features that are useable formatching to a representation of the first type of object, while a secondtype of object can include a second, different number of features thatare useable for matching to a representation of the second type ofobject.

Using a fixed threshold for the number of matching features forindicating whether or not a number of matching features indicates asuccessful match may result in some types of objects being unreliablydetected, if such types of objects do not contain a sufficient number ofuseable features to be able to satisfy the fixed number threshold. Onthe other hand, use of the fixed threshold for the number of matchingfeatures may result in a relatively large number of false positives whenperforming object recognition for other types of objects that mayinclude a larger number of useable features for matching.

Although it may be possible to manually tune thresholds forcorresponding different types of objects, such a manual tuning processcan be labor intensive and may be prone to human error.

In accordance with some implementations, instead of using a fixedthreshold for the number of matching features, a probabilistic model canbe used in an object recognition procedure to determine whether or notan object is present in visual data. In the ensuing discussion,reference is made to determining presence of an object in an image.However, in other implementations, an object recognition procedure usinga probabilistic model can be applied to determine presence of an objectin other types of visual data, such as video data and so forth.

The probabilistic model relates a probability to a number of matchingfeatures. The probability is a probability that an object is present inan image. By using the probabilistic model, instead of specifying afixed threshold for the number of matching features, a probabilitythreshold can be specified instead. The probability threshold can be thesame for different types of objects.

By using the probabilistic model, the determination of whether or not anobject recognition procedure has detected an object in an image is basedon the probability threshold. Multiple probabilistic models can beprovided for respective different types of objects.

In some implementations, to determine whether a particular object is inan image, an object recognition procedure can use a probabilistic model(corresponding to the particular object) to convert a number of matchesbetween an image and a corresponding object to a probability value. Theprobability value can be compared to the probability threshold todetermine whether a match is present that indicates that the particularobject is in the image.

In alternative implementations, the object recognition procedure can usea probabilistic model to convert the probability threshold to acorresponding number threshold, where the number threshold indicates anumber of matching features above which an object is considered to bepresent in an image. The number of matches between an image and acorresponding object can then be compared to the number threshold todetermine whether a match is present.

In the latter implementations, for each type of object, the respectiveprobabilistic model can relate the probability threshold to acorresponding number threshold. For different types of objects, therespective probabilistic models can relate the probability threshold todifferent number thresholds. By providing different number thresholdsbased on the probabilistic models rather than the same fixed numberthreshold, for different types of objects, more reliable objectrecognition for the different types of objects can be achieved.

In some examples, the multiple number thresholds for different types ofobjects can be stored in a lookup table or other lookup data structure.When attempting to determine whether a particular object is in an image,the lookup table or other lookup data structure can be accessed toretrieve the corresponding number threshold to use in the objectrecognition procedure.

Although reference is made to use of multiple probabilistic models fordifferent types of objects, it is noted that in alternativeimplementations, one probabilistic model can be used for multipledifferent types of objects. An input to the probabilistic model in suchimplementations would include an indication of the type of object, andthe probabilistic model can then use this type indication to perform therespective conversion between a probability value and a number ofmatching features.

In some implementations, the features that can be extracted from animage for comparison with a representation (e.g. model) of an object canbe point features. A point feature refers to an element (referred to asa “descriptor”) that is extracted from a given image patch (which canhave a predefined size, such as a 16×16 grid or other size). Within animage, a number of point features can be extracted, which can becompared to reference point features in the representation of an object.Examples of point features include SIFT (scale invariant featuretransforms) features. SURF (speeded up robust features), and others. Inother examples, instead of using point features, other types of featurescan be used, such as line features, block features, and so forth.

In the ensuing discussion, it is assumed that point features areextracted for performing object recognition in an image. Identifyingmatches between point features in an image and point features in arepresentation of the object is referred to as identifying pointcorrespondences. Although reference is made to matching point featuresin the ensuing discussion, it is noted that similar techniques can beapplied to matching other types of features in other implementations.

FIG. 1 illustrates an object recognition system 100 that includes anobject recognizer 102 according to some implementations. The objectrecognition system 100 can include a computer or a distributedarrangement of computers. The object recognizer 102 can be executed onone or multiple processors 104. The object recognizer 102 is able toaccess probabilistic models 106, which are stored in a storage medium(or storage media) 108, for use in performing an object recognitionprocedure to detect presence of an object in an image 110.

The image 110 can also be stored in the storage medium (or storagemedia) 108. The image 110 can be received by the object recognitionsystem 100 from a remote source, in some examples. One such remotesource can be the remote system 112 that is coupled to the objectrecognition system 100 over a network 114. The object recognition system100 can communicate over the network 114 through a network interface 115in the object recognition system 100. The remote system 112 can includea camera that captured the image 110. In alternative implementations,the camera can be part of the object recognition system 100.

The probabilistic models 106 can be produced by a probabilistic modelgenerator 103, which can be executed on the processor(s) 104 of theobject recognition system 100. In other examples, the probabilisticmodel generator 103 can be executed in a different system from theobject recognizer 102. In such latter examples, the probabilistic models106 generated by the probabilistic model generator 103 can be receivedby the object recognition system 100 from the system in which theprobabilistic model generator 103 executes.

An object descriptor database 116 can also be stored in the storagemedium (storage media) 108 of the object recognition system.Alternatively, the object descriptor database 116 can be stored in astorage subsystem that is outside the object recognition system 100. Theobject descriptor database 116 contains models of various differentobjects. Each object model contains descriptors (that describe pointfeatures or other types of features) of the corresponding object.

The object recognizer 102 can extract point features from the image 110,and can compare the extracted point features to the corresponding pointfeatures of the object models in the object descriptor database 116 todetermine whether a particular object (from among the objectsrepresented by the object models) is present in the image 110.

FIG. 2 is a process that can be performed by the object recognizer 102in accordance with some implementations. The process provides (at 202)the probabilistic models 106. Providing the probabilistic models 106 caninclude: (1) generating, in the object recognition system 100 by theprobabilistic model generator 103, the probabilistic models 106, or (2)receiving, by the object recognition system 100 from another system, theprobabilistic models 106.

The process of FIG. 2 further uses (at 204) the probabilistic models 106for detecting whether a particular object is in the image 110. Theobject recognizer 102 can determine a number of point correspondencesbetween the image 110 and a respective model of an object in the objectdescriptor database 116. As noted above, a point correspondence refersto a match between a point feature of the image 110 and a correspondingpoint feature of an object as represented by an object model.

In some implementations, the object recognizer 102 can use a respectiveprobabilistic model 106 to map the number of point correspondences to aprobability value, which can then be compared to a probabilitythreshold. A match can be indicated (to indicate that the correspondingobject is detected in the image 110) if the probability value exceedsthe predetermined probability threshold.

In alternative implementations, for a particular object, the respectiveprobabilistic model can be used to map the predetermined probabilitythreshold to a respective number threshold. The number thresholdspecifies a number of point correspondences above which a match of anobject to the image 110 is indicated. For detecting whether theparticular object is present in the image 110, the number of pointcorrespondences between the image 110 and the object model of theparticular object is compared to the number threshold mapped by theprobabilistic model, and a match is indicated if the number of pointcorrespondences exceeds the number threshold.

In further implementations, a probabilistic model can incorporateinformation regarding a camera pose. In such implementations, themapping between a probability value and a number of pointcorrespondences can differ depending on the relative pose of the camerato an object.

FIG. 3 is a process that can be performed by the object recognizer 102in accordance with further implementations. The object recognizer 102receives (at 302) the image 110. The object recognizer 102 then extracts(at 304) point features from the image.

The following tasks of FIG. 3 are performed for each of multiple objectmodels in the object descriptor database 116. For a model i of arespective object, the extracted point features from the image 110 arematched (at 306) to descriptors in model i in the object descriptordatabase 116. For each model i, the matching performed at 306 identifiesa set of point correspondences.

The object recognizer 102 then identifies (at 308) a subset of the pointcorrespondences in the set, where the point correspondences in thesubset are those that are consistent with a given view of the object.There may be multiple possible views of the object corresponding tomultiple possible relative poses (a pose can be defined by distance andangle) of a camera that captured the image 110.

It is possible that the set of point correspondences includes pointcorrespondences for respective different views. The set of pointcorrespondences may contain mismatches, resulting in different subsetsof the point correspondences being consistent with respective differentviews. In some implementations, the subset identified at 308 is themaximal subset, which is the subset that has the largest number of pointcorrespondences consistent with a given view of the object. This maximalsubset and the associated given view will also provide the camera pose(e.g. distance and angle) with respect to the object.

The identification of the maximal subset of point correspondences canuse a geometric consistency procedure. Given the set of pointcorrespondences identified at 306 based on matching the image 10 to themodel i in the object descriptor database 116, the geometric consistencytechnique attempts to select a maximal subset of the pointcorrespondences that are consistent with a single view of the object.

In some examples, the geometric consistency technique can be an RANSAC(RANdom SAmple Consensus) technique. An example RANSAC technique isdiscussed below in connection with FIG. 5.

The object recognizer 102 then uses (at 310) the respectiveprobabilistic model 106 (FIG. 1), for the type of object represented bymodel i, to determine whether the object represented by model i has beendetected in the image 110. As noted above, there are several alternativetechniques of making this determination. In some implementations, theobject recognizer 102 can use the respective probabilistic model 106 tomap the number of point correspondences in the maximal subset identifiedat 308 to a probability value, which can then be compared to aprobability threshold. A match can be indicated if the probability valueexceeds the probability threshold.

In alternative implementations, for the model i, the respectiveprobabilistic model 106 can be used to map the predetermined probabilitythreshold to a respective number threshold (a number of pointcorrespondences above which a match is indicated). The object recognizer102 can compare the number of point correspondences in the maximalsubset identified at 308 to the number threshold for model i todetermine whether there is a match. In some cases, a given probabilitythreshold may not be mappable by a probabilistic model to a numberthreshold. In such a case, an object can be deemed undetectable with aspecified confidence.

If the object recognizer determines (at 312) that a match between theimage 110 and the object represented by model i is indicated at 310,then the process can stop. If a match is not indicated, then the processof FIG. 3 can proceed to the next model in the object descriptordatabase 116 by updating i (at 314).

FIG. 4 depicts an example process of building a probabilistic model 106for a particular type of object (referred to as the “training object”),in accordance with some implementations. The process can be performed bythe probabilistic model generator 103 of FIG. 1, for example. Theprocess of FIG. 4 can be reiterated for each of multiple different typesof objects to build respective probabilistic models.

In alternative implementations, instead of building differentprobabilistic models for different types of objects, one probabilisticmodel can be produced, with the probabilistic model being able toperform different conversions between a probability value and arespective number of point correspondences for the different types ofobjects.

In the process of FIG. 4, the probabilistic model generator 103 receives(at 400) the training object. The point features of the training objectare extracted (at 402), and such extracted features are stored in amodel of the training object in the object descriptor database 116.

The process of FIG. 4 also generates (at 404) a number of simulatedviews of the training object. The simulated views are views thatcorrespond to different poses of a camera with respect to the trainingobject. For example, perspective or affine warps can be employed tocompute the simulated views.

Point features are extracted (at 406) from each of the simulated viewsof the training object. The extracted features for each simulated vieware then matched (at 408) to the object models in the object descriptordatabase 116. The matching performed at 408 uses a modified form of theobject recognition procedure depicted in FIG. 3, as explained furtherbelow. The output of the matching at 408 produces various statistics,which are obtained (at 410). A respective collection of statistics isoutput by the match of each of the simulated views to the object modelsin the object descriptor database 116. As discussed further below, suchstatistics can include mean values and variance values.

The matching performed at 408 is based on the ground truth that thetraining object is present (since the simulated views are images thatcontain different views of the training object). Therefore, thestatistics obtained at 410 are statistics for matches.

In addition, a separate sub-flow is provided in the process of FIG. 4 inwhich matching is performed with respect to reference images (containedin a corpus 412 of reference images) that are known to not include thetraining object. The process of FIG. 4 extracts (at 414) point featuresfrom each of the reference images. For each reference image, theextracted features are matched (at 416) to respective object models inthe object descriptor database 116. The matching performed at 416 uses amodified form of the object recognition procedure depicted in FIG. 3, asexplained further below.

A respective collection of multiple statistics is output based on thematching at 416 of a respective reference image to the object models isobtained (at 418). The matching performed at 416 is based on the groundtruth that there should not be a match to the corresponding object inthe object descriptor database 116. Therefore, the statistics obtainedat 418 are statistics for non-matches. As discussed further below, suchstatistics can include mean values and variance values.

The statistics obtained at 410 and 418 are then combined (at 420) tobuild a probabilistic model for the training object (discussed furtherbelow). In some implementations, the probabilistic model can be used todetermine (at 422) a number threshold, from the probability threshold,where the number threshold can be used in the object recognitionprocedure of FIG. 2 or 3. The computed number threshold is then stored(at 424) for later use.

In alternative implementations where the object recognition procedure ofFIG. 2 or 3 performs comparisons to a probability threshold, the tasks422 and 424 can be omitted, while the tasks 410, 418 and 420 are stillperformed.

An example probabilistic model that is built at 420 is discussed below,in accordance with some implementations. The probabilistic model has thefollowing random variables: M (ground truth index of an object in theobject descriptor database 116), O (empirical index of the object in theobject descriptor database 116), and N (number of inliers observedconsistent with the object). Predefined value(s) can be specified for Mand N to denote no object (an example of such predefined value can be −1or some other value).

An inlier can refer to a point correspondence between a point feature ofan image and a point feature of a model of an object. N can representthe number of such point correspondences. More generally, an inlier canrefer to any correspondence (match) between a feature of an image and afeature of an object model.

The ground truth index, M, is an identifier of a model in the objectdescriptor database 116 that represents the object that is actually inan image. The empirical index, O, is an identifier of a model in theobject descriptor database 116 that represents the object that an objectrecognition procedure believes that the procedure is looking at.

The geometric consistency procedure applied at 308 in FIG. 3 outputsvalues for O and N. The probabilistic model can be represented as aconditional probability P(M=m|O=m, N=n), where the probabilistic modelprovides the probability that a camera is actually looking at an object,M=m, given the measurements O (=m) and N (=n).

Applying Bayes' theorem twice, the conditional probability P(M=m|O=m,N=n) can be derived as follows:

${P\left( {{M = {{mO} = m}},{N = n}} \right)} = {\frac{{P\left( {M = m} \right)} \cdot {P\left( {{N = {{nM} = m}},{O = m}} \right)} \cdot {P\left( {O = {{mM} = m}} \right)}}{{P\left( {N = {{nO} = m}} \right)} \cdot {P\left( {O = m} \right)}}.}$

By marginalizing the first term in the denominator over M andsimplifying, the following is obtained:

${{P\left( {{M = {{mO} = m}},{N = n}} \right)} = \frac{{P\left( {M = m} \right)} \cdot {P\left( {{N = {{nM} = m}},{O = m}} \right)} \cdot {P\left( {O = {{mM} = m}} \right)}}{W}},{where}$W = P(N = nO = m, M = m) ⋅ P(M = m) ⋅ P(O = mM = m) + P(N = nO = m, M ≠ m) ⋅ P(M ≠ m) ⋅ P(O − mM ≠ m).

Each of the terms of the above equation for deriving the conditionalprobability P(M=m|O=m, N=n) is described below.

P(M=m) is a prior probability for the object m. This can be assumed tobe the same for all objects, in some examples. The prior probability,P(M=m), expresses an uncertainty about the probability of the object mprior to data being received.

P(N=n|M=m, O=m) is the probability of getting n inliers, given acorrectly observed object m. This probability can be estimated from thestatistics of the simulated views of the object m, obtained at 410 inFIG. 4. In some examples, a Gaussian distribution can be assumed with amean estimated by the empirical mean number of inliers for the correctdetections from simulated views of the object. The variance can also beestimated in this way. In other examples, the variance can be set equalto the mean. Setting variance equal to the mean is acceptable since thisis the limit of a Poisson distribution.

Given the above, the probability P(N=n|M=m, O=m) is derived as follows:

${{P\left( {{N = {{nM} = m}},{O = m}} \right)} = {\frac{1}{\sqrt{2{{\pi\mu}_{1}(m)}}} \cdot ^{{- {({n - {\mu_{1}{(m)}}})}^{2}}/{\mu_{1}{(m)}}}}},$

where μ₁(m) is the empirical mean number of inliers for correct matchesof object m. In this example, μ₁(m), along with the correspondingvariance, are the statistics obtained at 410. The foregoing equationassumes that mean is equal to variance.

P(O=m|M=m) is the probability of observing object m correctly. This canbe estimated from the statistics of the simulated views of the object(obtained at 410). In some examples, the probability can just be thefraction of detections that succeeded (in other words, the ratio of anumber of successful matches of the simulated views to respective objectmodels to a number of total matches performed).

P(M≠m) is the complement of the prior probability P(M=m) for the objectm. More specifically, P(M≠m)=1−P(M=m).

P(N=n|M≠m, O=m) is the probability of obtaining in inliers for anerroneous match against object m; in other words, when object m is notin fact in the image. This can be estimated from the statistics(obtained at 418) from matching the reference images against the objectmodel for object m. The process can use the same Gaussian model as notedabove, to derive the following:

${{P\left( {{N = {n{M \neq m}}},{O = m}} \right)} = {\frac{1}{\sqrt{2{{\pi\mu}_{2}(m)}}} \cdot ^{{- {({n - {\mu_{2}{(m)}}})}^{2}}/{\mu_{2}{(m)}}}}},$

where μ₂ (m) is the empirical mean number of inliers for erroneousmatches against object m. In the foregoing example, μ₂(m) along with theassociated variance, can be the statistics obtained at 418. Theforegoing equation assumes that mean is equal to variance.

According to the above, the probabilistic model provides a mappingbetween the probability of object m being in an image and a number ofinliers, t. As explained above, this mapping can be used to convert thenumber of inliers for an object into a probability value to be comparedto a probability threshold, which can be the same for all objects.Alternatively, by applying the inverse mapping, a probability thresholdcan be converted into an inlier count threshold, which is different foreach model.

FIG. 5 is a flow diagram of a geometric consistency procedure accordingto some implementations. FIG. 5 depicts a RANSAC (RANdom SAmpleConsensus) technique. As discussed above, the geometric consistencyprocedure is performed at 308 in the object recognition procedure ofFIG. 3 for matching an image to an object represented by a model of theobject descriptor database 116.

A score Best_Score is initialized (at 502) to zero (or some otherinitial value). A random sample of p (where p can be three or some othervalue) point correspondences are selected (at 504). This sample is thenused to generate up to three candidate camera poses, such as by using athree-point pose technique. In the three-point pose technique, threegeometric points can produce multiple poses of the camera.

For each candidate camera pose, the following is performed for eachpoint correspondence. Note that a point correspondence is associatedwith a point feature in the image and a matching point feature in theobject represented by a model of the object descriptor database 116. Theposition of the point feature in the object is re-projected (at 506)using the candidate camera pose. The re-projection effectively modifiesthe position of the point feature in the object to be consistent withthe candidate camera pose.

The distance between the re-projected point feature in the object andthe observed position of the corresponding point feature in the image isthen computed (at 508), where this distance is referred to as there-projection error. The re-projection error is compared (at 510) to anerror threshold. The process identifies a point correspondenceassociated with a re-projection error below the error threshold as beingan inlier.

The tasks 506, 508, and 510 are repeated for each of the other pointcorrespondences in the random sample (that has p point correspondences).

The process next counts (at 512) the number of point correspondenceswith re-projection errors below the error threshold. This counted numberis the score. If the counted score is greater than the current bestscore, Best_Score, then Best_Score is updated (at 514) to the countedscore. Also, the candidate camera pose associated with the highestcounted score so far is recorded (at 516).

The foregoing tasks are iterated until a stopping criterion isdetermined (at 518) to have been satisfied, where the stopping criterioncan be the best score, Best_Score, exceeding a predefined threshold, ora specified number of iterations have been performed. If the stoppingcriterion is not satisfied, then the tasks 504 to 518 are re-iterated,where task 504 would select another random sample of p pointcorrespondences.

After the stopping criterion is satisfied, then the best camera pose isdetermined (as recorded at 516), along with the corresponding countedscore, which represents the number of inliers.

As noted above, the matching performed at 408 or 416 uses a modifiedform of the object recognition procedure depicted in FIG. 3. In themodified object recognition procedure, the geometric consistencyprocedure performed at 308 in FIG. 3 uses an error threshold of zero (orsome other low value), which would lead to detection of larger numbersof inliers in the process described in FIG. 5.

Machine-readable instructions of modules described above (including theobject recognizer 102 and probabilistic model generator 103 of FIG. 1)are loaded for execution on one or multiple processors (such as 104 inFIG. 1). A processor can include a microprocessor, microcontroller,processor module or subsystem, programmable integrated circuit,programmable gate array, or another control or computing device.

Data and instructions are stored in respective storage devices, whichare implemented as one or more computer-readable or machine-readablestorage media. The storage media include different forms of memoryincluding semiconductor memory devices such as dynamic or static randomaccess memories (DRAMs or SRAMs), erasable and programmable read-onlymemories (EPROMs), electrically erasable and programmable read-onlymemories (EEPROMs) and flash memories; magnetic disks such as fixed,floppy and removable disks; other magnetic media including tape; opticalmedia such as compact disks (CDs) or digital video disks (DVDs); orother types of storage devices. Note that the instructions discussedabove can be provided on one computer-readable or machine-readablestorage medium, or alternatively, can be provided on multiplecomputer-readable or machine-readable storage media distributed in alarge system having possibly plural nodes. Such computer-readable ormachine-readable storage medium or media is (are) considered to be partof an article (or article of manufacture). An article or article ofmanufacture can refer to any manufactured single component or multiplecomponents. The storage medium or media can be located either in themachine naming the machine-readable instructions, or located at a remotesite from which machine-readable instructions can be downloaded over anetwork for execution.

In the foregoing description, numerous details are set forth to providean understanding of the subject disclosed herein. However,implementations may be practiced without some or all of these details.Other implementations may include modifications and variations from thedetails discussed above. It is intended that the appended claims coversuch modifications and variations.

What is claimed is:
 1. A method comprising: providing, by a systemhaving a processor, a probabilistic model based on an output of amatching procedure that matches a particular object to representationsof objects, the probabilistic model relating a probability of an objectbeing present to a number of matching features, and using, by thesystem, the probabilistic model for detecting whether a particularobject is present in received visual data.
 2. The method of claim 1,wherein providing the probabilistic model comprises building theprobabilistic model at the system.
 3. The method of claim 1, whereinproviding the probabilistic model comprises receiving the probabilisticmodel at the system from another system.
 4. The method of claim 1,wherein the probabilistic model is further based on an output of amatching procedure that matches a reference image without the particularobject to the representations of objects.
 5. The method of claim 1,wherein the probabilistic model is based on outputs of matchingprocedures that match respective simulated views of the particularobject to the representations of objects, the simulated viewscorresponding to different poses of a camera with respect to theparticular object.
 6. The method of claim 1, wherein using theprobabilistic model comprises: determining a number of matching featuresbetween the received visual data and a corresponding one of therepresentations of objects; calculating a probability value by theprobabilistic model in response to the determined number of matchingfeatures; and comparing the probability value to a predeterminedprobability threshold.
 7. The method of claim 1, wherein using theprobabilistic model comprises: determining a number of matching featuresbetween the received visual data and a corresponding one of therepresentations of objects; calculating a number threshold by theprobabilistic model in response to a predefined probability threshold;and comparing the determined number of matching features to the numberthreshold.
 8. The method of claim 1 further comprising: providing anadditional probabilistic model for an additional object; and using theadditional probabilistic model for detecting whether the additionalobject is present in received visual data.
 9. The method of claim 8,wherein using the probabilistic models for detecting the objects arepresent in respective visual data employs a probability threshold. 10.An article comprising at least one machine-readable storage mediumstoring instructions that upon execution cause a system to: receive agiven object; match features of the given object to representations ofobjects; match features of a reference image that does not include thegiven object to the representations of objects; and build aprobabilistic model based on outputs of matching the features of thegiven object and matching the features of the reference image, whereinthe probabilistic model is useable in an object recognition procedure todetermine whether an object is in visual data.
 11. The article of claim10, wherein matching the features of the given object to therepresentations of objects comprises matching features of simulatedviews of the given object to the representations of objects, thesimulated views corresponding to different poses of a camera withrespect to the given object.
 12. The article of claim 10, wherein theprobabilistic model provides a conditional probability that the givenobject is in visual data given a number of matching features.
 13. Thearticle of claim 10, wherein the instructions upon execution cause thesystem to further: receive the visual data; determine a number ofmatching features between the visual data and a given one of therepresentations of the objects; and use the probabilistic model and thedetermined number of matching features to determine whether the objectrepresented by the given representation is present in the visual data.14. The article of claim 13, wherein using the probabilistic model isbased on a probability threshold.
 15. A system comprising: at least oneprocessor to: provide a probabilistic model based on an output of amatching procedure that matches a particular object to representationsof objects, the probabilistic model relating a probability of an objectbeing present to a number of matching features; and use theprobabilistic model for detecting whether a particular object is presentin received visual data.